GIS-Based Study of Lightning Damages

Lin Cao, Wei-Ning Xiang, and Joseph C. WilsonDepartment of Geography and Earth Sciences
University of North Carolina
Charlotte, NC

(Republished with permission)

ABSTRACT

Lightning can cause severe damage to property. In Charlotte,
North Carolina, from July 1993 to July 1996, lightning strikes caused
52 fires with $1.3 million in damage. A lot of researches have been done
in the area of lightning damage protection, but rarely has study looked
at the geographic correlation and distribution of the lightning damage.
The development of the GIS (Geographic Information System) provides a
new technique in displaying and manipulating of geographic information.

In our study, this new method was applied, and the geographic
correlation of the lightning damage with several environmental and socio-economic
variables were examined. Lightning strikes that caused damage from January
1993 to December 1995 in Mecklenburg County, North Carolina were geocoded
and the possible areas where lightning strikes could occur were located,
and the geographical pattern of lightning damage was studied. In addition,
the statistical correlation between the cost of the lightning damage and
different variables was analyzed.

INTRODUCTION

Lightning strikes cause tremendous losses each year and pose
threat to property. For instance, in Charlotte, North Carolina, from 1993
to 1995, eleven lightning strikes caused over $100,000 damages. Also,
everyone could have the chance of damaging strikes. It has been estimated
that a home owner can expect a damaging strike once every 100 to 200 years.

In this exploratory study, we reviewed the cost of lightning
strikes that occurred in Mecklenburg County, North Carolina from January
1993 to December 1995. We were trying to find out the geographic distribution
of the cost of lightning damage and whether any environmental or socio-economic
variable had significant effect on the cost of lightning damage. If we
could find the
pattern of geographic distribution of the cost of lightning damage or
significant correlation between certain variables with the cost of lightning
damage, we could predict the potential high damage area of lightning strikes.
It would be very helpful in future planning of this area, and it will
be helpful in avoiding the high damages from lighting strikes.

GIS was applied in this study. GIS is a newly developed computer
software which can capture, store, manage, extract and display geographically
referenced information. It provide statistical summaries, calculations,
interrelationships of data, buffer generation and overlay functions. In
our study, we used ARC/INFO -a major software in GIS. The geocoding of
the Lightning strike, locating of the exact area of lightning strikes,
integrating of the environmental variables and socio-economic variables
with
the cost of lightning damage, and finding the geographic pattern of the
cost of lightning damage were all accomplished by the functions in ARC/INFO.

The geographic pattern of the lightning damage was studied
and we found most of the high cost of lightning damage occurred in the
South Planning District in Mecklenburg County. Correlation and regression
analysis were performed in testing the interrelationships of the environmental
and socio-economic variables with the cost of lighting damage. The environmental
variables included soil types, pH value, water capacity and slope. The
socio-economic variables included the age of the building and the property
value.

This study demonstrated the ability of GIS method to locate
the possible area of the lighting strikes. This enables the linking of
the geographic and socio-economic variables with the cost of lightning
damage and makes the study of the causes of lightning damage feasible.

METHODOLOGY

The lightning damage assessment composed of four steps. The
first step is the Data Collection, the second step is the GIS Data Base
Development, the third step is the Classification of Lightning Damage
and the four step is the study of the Geographic Correlation.

1. Data Collection

The lightning damage data were obtained from Mecklenburg County
Fire Department. These included 95 cases of lightning damages from January
1993 to December 1995 with the street addresses and the cost measured
in dollars.

The environmental variables included soil type, pH value,
water capacity and slope. They came from the Soil Conservation Survey
(SCS). The socio-economic variables included the age of the building and
the property value. They were obtained from Zoning and Addressing Department
in Mecklenburg County.

In this study, several other GIS data layers were used. Streets
centerline file in 1:100,000 scale updated in June 1996 was used in the
geocoding and locating of the area of lightning damage. The Planimetric
layer in scale 1:2,400 obtained from a 1992 airphoto was used in the locating
of the lightning stricken area. Soil coverage in scale 1:24,000 was used
in the integration of data. They all were obtained from the Engineering
Department of Mecklenburg County.

2. GIS Database Development

The database development included the following two steps:

1. The first step of the data base development was to find
the possible area for each lightning strike that caused damage. There
are two types of lightning damage: One is caused by direct hits from lightning
strikes, which means lightning directly hits a building and causes damage;
the other is caused by indirect hits, such as lightning hits a tree and
travels into a house through a power line and causes damage. The lighting
damage data obtained in this study were all 911 calls and had the exact
address of each building. Since no records were obtained to show whether
these damage were from direct hits or indirect hits of lighting strikes,
all the possible area that lightning strike could occur should be included
in our study. And it could be an polygon area surrounding each building.
In this study, the following steps was performed to define the area of
lightning strikes.

First, the lightning strikes were geocoded through the ADDRESSMATCH
function. The streets centerline file was used and a point coverage
was generated. This point layer contains the information of the address
and cost of lightning damage. About 99% percent of the ninety-five records
were matched.

Secondly, the exact location of the building was found for
each lightning strike. This was accomplished by using the Planimetric
layer and the tax parcel information. Since the data we studied were
for 1993 to 1995, we found several buildings missed from the Planimetric
layer (produced in 1992 ) . Using the streets centerline file as a base
map, we manually added the polygons to the
sites where the tax parcel information indicated. After this step, a
polygon coverage containing all the building roof areas of lightning
strikes was generated.

Thirdly, a 1,000 feet buffer was generated around each building.
This created a study area which covered all the possible sites that
lightning strikes could occur. This polygon coverage was used as study
area in the our analysis.

2. In the second step of database development, all the environmental
and socio-economic variables that we wanted to test was integrated with
the cost of lightning damage. First, the address and cost of lighting
damage was added to the PAT file of the buffered study area from the point
coverage of lightning damage from the former step by OVERLAY and JOINITEM
function. Secondly, the overlay of the study area with the soil coverage
was performed and the information of the type of soil was joined into
the PAT file of the output coverage. By ADDITEM and JOIITEM function,
soil types, pH value, water capacity, the age of each building and property
value of the parcel were also added into the same PAT file. This PAT file
also contained the addresses and cost of lightning damages. At this point,
we integrated all the variables that we wanted to test with the cost of
lightning damage, and we were ready to do the analysis.

Lightning Damage Classification

In order to show the pattern of the cost of the lightning
damage, all the data were classified into five categories based on the
amount of cost from lightning damage:

Slight Damage: $10 to $1,000,

Minor Damage: $1,000 to $2,500,

Moderate Damage: $2,500 to $10,000,

Major Damage: $10,000 to $45,000,

Severe Damage: $45,000 to $165,000.

Geographic Correlation

The distribution of the cost of lightning damage was studied
by overlaying it with the layer of the Planning District and the land
use in Mecklenburg County. Statistical function in ARC/INFO was performed,
and the frequency was calculated for each Planing District by the total
number of lightning strikes and also by each category of the cost from
lightning damage. The geographical correlation between environmental and
socio-economic variables, and the cost of lightning damage was tested.
The statistical correlation of these variables with the cost of lightning
damage was also examined in SYSTAT.

RESULTS

Mecklenburg County is divided into seven Planning Districts.
The study showed that all the lightning damage occurred in or close to
the South Planning District which includes some areas of the Central,
Southwest, South and East Districts. About 56% of lightning damage occurred
in the South District, 23% occurred in the Central District, 17% occurred
in the East District and 4% occurred in the Southwest District. No record
was found in the North, Northeast and Northwest Districts. Also, based
on our classification of the cost of lightning damage, most of the tremendous
damage occurred in the South Planning District which include all the Severe
Damage, 87.5% of the Major Damage and 50% of the Moderate Damage ( see
the classification of the cost above ).

The study of environmental variables shows that the 62% of
the slope was 5o , 56% of the pH value was 5.1, 67% of the water capacity
ranged between 0.14 to 0.15 and about 62% of all the soils was CeB2 or
CeD2. The result of the socio-economic variables showed that 90% of all
the building was built after 1950 and 50% of the property value was over
$100,000, while comparing with the 22% of the cost of lightning damage
was over $100,000. This illustrated the severity of lightning damage.
Correlation and regression analysis were performed for these factors with
the cost of lightning damage in SYSTAT but no significant result was found.

CONCLUSIONS

From this study, we found the majority of lightning damage
during 1993 to 1995 occurred in the South Planning District. This District
has historically absorbed much of the suburban growth in Mecklenburg County
and contains the typical single family residences. The area where the
lightning damage occurred has been one of the primary locations for new
residential developments in this area. And based on the South District
Plan for the year 2015 (Mecklenburg Planning Commission, June 1,
1992), we know one of the focus of development in this area will be the
development of a wide range of housing types and identities residential
area. Although we could not find the significant component which influenced
the cost of lightning damage, the geographic pattern showed in this study
should bring up a concern about the future planning in this area. Further
study in this area would be very important for the planners and residents.

There are some limitations of this study. First, there was
no significant correlation found between the cost of lightning damage
and the variables we chose in this study. One reason for this could be
the limitation of lightning damage data. The data that were available
at the beginning of our study were for 1993 to 1995 and contained 95 cases.
There is a possibility that these data could not show the statistical
correlation. Secondly, some of the data were not available at the time
of our study. All these limitations could affect the statistical results.

This study showed the feasibility and capability of applying
GIS to the study of Lightning Damage. In testing the geographic distribution
of the lightning damage, we found some geographic pattern of lightning
damages. However, this study is still at preliminary stage. More variables
such as the sea level, building height, aspect of slope and construction
of buildings should be analyzed. We prospect this study will lead more
thorough and intensive studies of this field.